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研究生:張志謙
研究生(外文):Chih-Chien Chang
論文名稱(外文):Exploration of hybrid gain data assimilation algorithm for numerical weather prediction
指導教授:楊舒芝
指導教授(外文):Shu-Chih Yang
學位類別:博士
校院名稱:國立中央大學
系所名稱:大氣科學學系
學門:自然科學學門
學類:大氣科學學類
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:199
中文關鍵詞:資料同化混成增益矩陣資料同化福衛七號掩星觀測
外文關鍵詞:Data AssimilationHybrid Gain Data AssimilationFORMOSAT-7/COSMIC2Radio Occultation
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此論文將透過觀測系統模擬實驗(Observing System Simulation Experiment, OSSE)探討兩個主題,一是提出一種新的混成同化方法改善混成增益矩陣(Hybrid gain)資料同化系統,以避免在混成資料同化方法中,使用需基於經驗或人為給定之權重係數以結合子同化系統資訊。二是探討於區域模式WRF (Weather Research and Forecasting)中,比較使用不同的資料同化方法同化福衛三號(FS-3)以及福衛七號(FS-7)掩星觀測資料(Radio Occultation, RO)之同化效益。

混成資料同化方法(Hybrid data assimilation)結合變分與系集資料同化方法之優勢,但傳統的混成資料同化方法皆需給定一個權重係數結合子同化系統的資訊,此係數對混成資料同化方法的表現有舉足輕重的影響。為客觀呈現混成資料同化方法的優勢,本文提出新的混成資料同化方法(QR-HGDA),於變分子同化系統進行分析修正時,僅採用與系集正交之修正量(正交向量)更新,此更新方法可避免主觀決定權重係數。本論文所提出之混成資料同化方法已成功應用於準地轉模式中。透過一系列敏感性實驗的研究,我們建議使用混成增益矩陣資料同化系統時,應重新建立靜態(非流場相依)之背景誤差結構以優化混成同化系統表現,而非使用傳統變分系統之統計背景誤差結構。

本研究也利用WRF-3DVAR,WRF-LETKF同化系統將混成增益矩陣資料同化方法建構於WRF模式中(WRF-HGDA),並比較分別同化福衛三號及福衛七號的表現。研究結果顯示,當觀測密度不足時,透過WRF-3DVAR所提供的第二階段修正,WRF-HGDA仍可在觀測密度較低的區域中有效地降低背景場的誤差,改善同化表現。即使已根據準地轉模式的經驗,調整WRF-3DVAR之靜態背景誤差矩陣的結構,但當觀測密度增加後,受限於WRF-3DVAR靜態的背景誤差矩陣結構,WRF-HGDA雖在水氣場與風場仍具優勢,但於溫度場的表現反而不如WRF-LETKF。區域模式實驗中獲得的結果同樣表示出重建背景誤差結構之重要性。除藉由調整權重係數改善WRF-HGDA的同化表現外,我們更建議直接將QR-HGDA應用於WRF模式,透過正交向量的更新,完整發揮WRF-3DVAR的同化效益。

雖然整體同化結果顯示,WRF-LETKF只在溫度場較WRF-HGDA佳。但當掩星觀測數量增加後,WRF-LETKF的改善量卻最為顯著。本研究因而採用WRF-LETKF同化系統進一步探討同化福衛七號掩星觀測對旋生發展的影響。此實驗將OSSE中真實場(Nature Run)的解析度提高至三公里,再利用WRF-LETKF系統同化這些來自高解析度真實場的觀測。相對於颶風Helene的高可預報度,即使提高解析度,颶風Gordon的旋生與增強過程仍不易掌握。研究結果顯示,利用WRF-LETKF系統同化福衛七號的掩星觀測資料比同化福衛三號的掩星觀測更能提供合適的大氣環境。從機率預報的角度,同化福衛七號掩星觀測有助於改善氣旋旋生的預報,且對於後續氣旋的增強和維持都有正面助益,並可進而改進降雨預報。
This dissertation aims at exploring two major issues with an Observing System Simulation Experiment (OSSE) configuration:
(1) Exploring the feasibility to eliminate an artificial combination weighting parameter in hybrid data assimilation,
(2) Evaluating the benefits of assimilating the GPS RO (Radio Occultation) observation with hybrid gain data assimilation (HGDA).

Traditional hybrid data assimilation requires an empirically estimated parameter to combine information from its component data assimilation (DA) systems. The performance of the hybrid DA system highly relies on this parameter. We therefore motivated to develop a parameterless hybrid DA algorithm. By limiting the variational correction to the subspace orthogonal to the ensemble perturbation subspace, the modified algorithm (QR-HGDA) is attainable with a quasi-geostrophic model. Our results suggest that a well-tuned static background error covariance for pure 3DVAR is not necessarily the optimal candidate for the use in hybrid DA. It implies the imperative of evaluating the optimality of the static B matrix for hybrid algorithms.

The parameter-dependent HGDA algorithm was implemented in the regional WRF model (WRF-HGDA) with the WRF-3DVAR and WRF-LETKF systems. To evaluate the benefits of RO observation, the synthetic RO observations were generated based on the real observation location from FORMOSAT-3/COSMIC (FS-3) and the simulated observation location of FORMOSAT-7/COSMIC2 (FS-7). Results indicate that the WRF-HGDA is superior to its component systems as the observation is sparse. With a dense observation network, the WRF-HGDA has the smallest RMSE in moisture and wind field while the WRF-LETKF outperforms the other two systems in temperature field. Although the static B matrix used in WRF-3DVAR has been tuned, it is unable to further improve the WRF-HGDA, echoing the imperative of evaluating the optimality of the static B matrix for the hybrids. Adjusting the combination weight improves the performance of WRF-HGDA while applying the QR-HGDA might be recommended.

Besides, to evaluate the benefits of FS-7 observation, an experiment with a higher resolution nature run was conducted with the WRF-LETKF to focus on the TC genesis. In contrast to the high predictability of Hurricane Helene, it is challenging to simulate the generation of Hurricane Gordon. Results show that assimilating the FS-7 observation leads to an environment that favors the TC genesis while the assimilation of FS-3 exhibits a drier environment and Hurricane Gordon’s structure is less robust in the FS-3 analysis. From the probabilistic perspective, assimilating the FS-7 observation leads to a positive impact on predicting the TC genesis and the heavy rainfall.
摘要 I
Abstract III
Table of Contents V
List of Figures VIII
List of Tables XVIII

CHAPTER 1 Introduction 1
1.1 Background 1
1.2 Approach 7
1.3 Objectives 8
1.4 Outline 10

CHAPTER 2 Hybrid gain data assimilation (HGDA) algorithms 11
2.1 Hybrid data assimilation 11
2.2 Hybrid Gain Data Assimilation (HGDA) 15
2.3 Modified HGDA with VAR update limited to the orthogonal subspace 19

CHAPTER 3 Investigations of QR-HGDA on QG-model 23
3.1 QG model 23
3.2 Experimental Setup 24
3.2.1 Default setup for DA systems 24
3.2.2 Setup of the sensitivity experiments 25
3.3 Results with Default DA setup 28
3.4 Results with Sensitivity Experiments 34
3.4.1 Sensitivity to model bias 34
3.4.2 Sensitivity to observation error estimation 35
3.4.3 Sensitivity to background error estimation 37
3.5 Discussion of Global orthogonality and local orthogonality 42
3.6 Short Summary 44

CHAPTER 4 An observation Simulation system Experiment (OSSE) with GPS RO observation 47
4.1 Introduction of RO observation 48
4.1.1 Radio Occultation technique 49
4.1.2 FORMOSAT-3/COSMIC (FS-3) and FORMOSAT-7/COSMIC2 (FS-7) missions 52
4.2 Experimental configuration 52
4.2.1 Configuration of numerical model 52
4.2.2 Configuration of Data assimilation systems 53
4.2.3 Experiment design 55
4.2.4 Synthetic observation generation 56
4.3 Results of DA systems w/o RO observation 58
4.3.1 Single point observation experiment 59
4.3.2 Cycling run experiments with conventional observations 62
4.4 Results of DA systems with conventional and RO observations 65
4.5 Discussion on the static B matrix used in WRF-3DVAR 70
4.6 Short summary 73

CHAPTER 5 Comparison of FS-3 and FS-7 observation on hurricane prediction 75
5.1 Experimental setup 76
5.1.1 Nature Run analysis 76
5.1.2 Synthetic Observation generation 77
5.2 Results of DA cycles 78
5.3 Results of ensemble forecast 79
5.4 Short summary 82

CHAPTER 6 Conclusion and future research 83

Glossary of Mathematical Quantities 88
Acronyms 90
Bibliography 92
Tables 107
Figures 117
Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Phys. D Nonlinear Phenom., 230, 99–111, doi:10.1016/j.physd.2006.02.011
Anthes, R. A., 2011: Exploring Earth’s atmosphere with radio occultation: contributions to weather, climate and space weather. Atmos. Meas. Tech., 4, 1077–1103, doi:10.5194/amt-4-1077-2011.
Anthes, R. A., and Coauthors, 2008: The COSMIC/FORMOSAT-3 Mission: Early Results. Bull. Amer. Meteor. Soc., 89, 313–334, doi:10.1175/BAMS-89-3-313.
Aparicio, J. M., and G. Deblonde, 2008: Impact of the assimilation of CHAMP refractivity profiles on environment Canada global forecasts. Mon.Wea. Rev., 136, 257–275, doi:10.1175/2007MWR1951.1.
Arnold, C. P., and C. H. Dey, 1986: Observing-systems simulation experiments: past, present and future. Bull. Amer. Meteor. Soc., 67, 687–695,
doi:10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2.
Bannister, R. N., 2017: A review of operational methods of variational and ensemble-variational data assimilation. Q. J. R. Meteorol. Soc., 143, 607–633, doi:10.1002/qj.2982.
Barker, D. M., 1998: Var scientific development paper 25: The use of synoptic-dependent error structure in 3DVAR. UK Met Office Tech. Rep., 2 pp. [Available from the Met Office, Saughton House, Broomhouse Dr., Edinburgh EH11 3XQ, United Kingdom.].
Bauer, P., G. Radnóti, S. Healy, and C. Cardinali, 2014: GNSS radio occultation constellation observing system experiments. Mon.Wea. Rev., 142, 555–572, doi:10.1175/MWR-D-13-00130.1.
Bishop, C. H., J. S. Whitaker, and L. Lei, 2017: Gain form of the ensemble transform Kalman Filter and its relevance to satellite data assimilation with model space ensemble covariance localization. Mon.Wea. Rev., 145, 4575–4592,
doi:10.1175/MWR-D-17-0102.1.
Bonavita, M., 2014: On some aspects of the impact of GPSRO observations in global numerical weather prediction. Q. J. R. Meteorol. Soc., 140, 2546–2562, doi:10.1002/qj.2320.
——, M. Hamrud, and L. Isaksen, 2015: EnKF and Hybrid Gain Ensemble Data Assimilation. Part II: EnKF and Hybrid Gain Results. Mon.Wea. Rev., 143, 4865–4882, doi:10.1175/MWR-D-15-0071.1.
Bormann, N., M. Bonavita, R. Dragani, R. Eresmaa, M. Matricardi, and A. Mcnally, 2016: Enhancing the impact of IASI observations through an updated observation-error covariance matrix. Q. J. R. Meteorol. Soc., 142, 1767–1780, doi:10.1002/qj.2774.
Boukabara, S. A., and Coauthors, 2016: Community Global Observing System simulation experiment (OSSE) package (CGOP): Description and usage. J. Atmos. Ocean. Technol., 33, 1759–1777, doi:10.1175/jtech-d-16-0012.1.
——, and Coauthors, 2018: Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Perfect observations simulation validation. J. Atmos. Ocean. Technol., 35, 207–226, doi:10.1175/JTECH-D-17-0077.1.
Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Q. J. R. Meteorol. Soc., 131, 1013–1043, doi:10.1256/qj.04.15.
——, P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part II: One-Month Experiments with Real Observations. Mon.Wea. Rev., 138, 1567–1586, doi:10.1175/2009MWR3158.1.
Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc., 135, 239–250, doi:10.1002/qj.366.
——, and F. Prates, 2011: Performance measurement with advanced diagnostic tools of all-sky microwave imager radiances in 4D-Var. Q. J. R. Meteorol. Soc., 137, 2038–2046, doi:10.1002/qj.865.
——, and S. Healy, 2014: Impact of GPS radio occultation measurements in the ECMWF system using adjoint-based diagnostics. Q. J. R. Meteorol. Soc., 140, 2315–2320, doi:10.1002/qj.2300.
Carrassi, A., A. Trevisan, L. Descamps, O. Talagrand, and F. Uboldi, 2008: Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: A comparison with the EnKF. Nonlinear Process. Geophys., 15, 503–521, doi:10.5194/npg-15-503-2008.
Caya, A., J. Sun, and C. Snyder, 2005: A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation. Mon.Wea. Rev., 133, 3081–3094, doi:10.1175/MWR3021.1.
Chang, C.-C., S.-C.Yang, and C. Keppenne, 2014: Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011). J. Meteorol. Soc. Japan. Ser. II, 92, 559–584, doi:10.2151/jmsj.2014-604.
——, S. G. Penny, and S.-C. Yang, 2020a: Hybrid Gain Data Assimilation using Variational Corrections in the Subspace Orthogonal to the Ensemble. Mon.Wea. Rev., 148, 2331–2350, doi:10.1175/MWR-D-19-0128.1.
Chang, Y. P., S. C.Yang, K. J.Lin, G. Y.Lien, andC. M.Wu, 2020b: Impact of tropical cyclone initialization on its convection development and intensity: A case study of Typhoon megi (2010). J. Atmos. Sci., 77, 443–464, doi:10.1175/JAS-D-19-0058.1.
Chen, S.-Y., C.-Y. Huang, Y.-H. Kuo, Y.-R. Guo, and S. Shiau, 2009: Assimilation of GPS Refractivity from FORMOSAT-3/COSMIC Using a Nonlocal Operator with WRF 3DVAR and Its Impact on the Prediction of a Typhoon Event. Terr. Atmos. Ocean. Sci., 20, 133, doi:10.3319/TAO.2007.11.29.01(F3C).
Chen, S. Y., H. Zhao, and C. Y. Huang, 2018a: Impacts of GNSS Radio Occultation Data on Predictions of Two Super-Intense Typhoons with WRF Hybrid Variational-Ensemble Data Assimilation. J. Aeronaut. Astronaut. Aviat., 50, 347–364, doi:10.6125/JoAAA.201812_50(4).02.
Chen, X. M., and Coauthors, 2018b: The impact of airborne radio occultation observations on the simulation of Hurricane Karl (2010). Mon.Wea. Rev., 146, 329–350, doi:10.1175/MWR-D-17-0001.1.
Chen, Y., and C. Snyder, 2007: Assimilating vortex position with an ensemble Kalman filter. Mon.Wea. Rev., 135, 1828–1845, doi:10.1175/MWR3351.1.
Chen, Y., S. R. H. Rizvi, X. Y. Huang, J. Min, and X. Zhang, 2013: Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions. Meteorol. Atmos. Phys., 121, 79–98, doi:10.1007/s00703-013-0251-y.
Chen, Y. C., M. E. Hsieh, L. F. Hsiao, Y. H. Kuo, M. J. Yang, C. Y. Huang, and C. S. Lee, 2015: Systematic evaluation of the impacts of GPSRO data on the prediction of typhoons over the northwestern Pacific in 2008-2010. Atmos. Meas. Tech., 8, 2531–2542, doi:10.5194/amt-8-2531-2015.
Clayton, A. M., A. C. Lorenc, and D. M. Barker, 2013: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Q. J. R. Meteorol. Soc., 139, 1445–1461, doi:10.1002/qj.2054.
Cook, K., M. J. Wenkel, C. J. Fong, N. Yen, and G. S. Chang, 2016: From paper to production: Status update for the COSMIC-2/FORMOSAT-7 program. IEEE Aerosp. Conf. Proc., 2016-June, doi:10.1109/AERO.2016.7500755.
Corazza, M., and Coauthors, 2003: Use of the breeding technique to estimate the structure of the analysis “errors of the day.” Nonlinear Process. Geophys., 10, 233–243, doi:10.5194/npg-10-233-2003.
——, E.Kalnay, and S. C. Yang, 2007: An implementation of the Local Ensemble Kalman Filter in a quasi geostrophic model and comparison with 3D-Var. Nonlinear Process. Geophys., 14, 89–101, doi:10.5194/npg-14-89-2007.
Cucurull, L., and M. J. Mueller, 2020: An analysis of alternatives for the COSMIC-2 constellation in the context of global observing system simulation experiments. Weather Forecast., 35, 51–66, doi:10.1175/WAF-D-19-0185.1.
Cucurull, L., J. C. Derber, R. Treadon, and R. J. Purser, 2007: Assimilation of Global Positioning System Radio Occultation Observations into NCEP’s Global Data Assimilation System. Mon.Wea. Rev., 135, 3174–3193, doi:10.1175/MWR3461.1.
Cucurull, L., R. A. Anthes, and L.-L. Tsao, 2014: Radio Occultation Observations as Anchor Observations in Numerical Weather Prediction Models and Associated Reduction of Bias Corrections in Microwave and Infrared Satellite Observations. J. Atmos. Ocean. Technol., 31, 20–32, doi:10.1175/JTECH-D-13-00059.1.
Cucurull, L., R. Li, and T. R .Peevey, 2017: Assessment of radio occultation observations from the COSMIC-2 mission with a simplified observing system simulation experiment configuration. Mon.Wea. Rev., 145, 3581–3597, doi:10.1175/MWR-D-16-0475.1.
Cucurull, L. , R. Atlas, R. Li, M. J. Mueller, and R. N. Hoffman, 2018: An observing system simulation experiment with a constellation of Radio Occultation Satellites. Mon.Wea. Rev., 146, 4247–4259, doi:10.1175/MWR-D-18-0089.1.
English, S., and Coauthors, 2013: Impact of Satellite Data. Tech. Memoradum ECMWF, 46.
Errico, R. M., R. Yang, N. C. Privé, K. S. Tai, R. Todling, M. E. Sienkiewicz, and J. Guo, 2013: Development and validation of observing-system simulation experiments at NASA’s global modeling and assimilation office. Q. J. R. Meteorol. Soc., 139, 1162–1178, doi:10.1002/qj.2027.
Gobiet, A., G. Kirchengast, G. L. Manney, M. Borsche, C. Retscher, and G. Stiller, 2007: Retrieval of temperature profiles from CHAMP for climate monitoring: Intercomparison with Envisat MIPAS and GOMOS and different atmospheric analyses. Atmos. Chem. Phys., 7, 3519–3536, doi:10.5194/acp-7-3519-2007.
Golub, G. H. and C. F. Van Loan, 2013: Matrix Computations. 4th Edition, Johns Hopkins University Press, Baltimore.
Goodliff, M., J. Amezcua, and P. J. VanLeeuwen, 2015: Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity. Tellus A Dyn. Meteorol. Oceanogr., 67, 26928, doi:10.3402/tellusa.v67.26928.
Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and ensemble Kalman filter localization techniques. Mon.Wea. Rev., 139, 511–522, doi:10.1175/2010MWR3328.1.
Ha, J. H., J. H. Kang, and S. J. Choi, 2018: The impact of vertical resolution in the assimilation of GPS radio occultation data. Weather Forecast., 33, 1033–1044, doi:10.1175/WAF-D-17-0061.1.
Hajj, G. A., and Coauthors, 2004: CHAMP and SAC-C atmospheric occultation results and intercomparisons. J. Geophys. Res. D Atmos., 109, 1–24, doi:10.1029/2003jd003909.
Hamill, T. M., and C. Snyder, 2000: A Hybrid Ensemble Kalman Filter–3D Variational Analysis Scheme. Mon.Wea. Rev., 128, 2905–2919, doi:10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2.
Han, B., Y. Morton, E. Gunawan, and D. Xu, 2019: Planetary Boundary Layer Height Detection Using Mountaintop-Based GNSS Radio Occultation Signal Amplitude. IEEE Trans. Geosci. Remote Sens., 57, 4332–4348, doi:10.1109/TGRS.2018.2890676.
Harnisch, F., S. B. Healy, P. Bauer, and S. J. English, 2013: Scaling of GNSS radio occultation impact with observation number using an ensemble of data assimilations. Mon.Wea. Rev., 141, 4395–4413, doi:10.1175/MWR-D-13-00098.1.
He, W., S. Ho, H. Chen, X. Zhou, D. Hunt, and Y.-H. Kuo, 2009: Assessment of radiosonde temperature measurements in the upper troposphere and lower stratosphere using COSMIC radio occultation data. Geophys. Res. Lett., 36, , doi:10.1029/2009GL038712.
Healy, S. B., 2008: Forecast impact experiment with a constellation of GPS radio occultation receivers. 118, 111–118, doi:10.1002/asl.
Healy, S. B., A. M. Jupp, and C. Marquardt, 2005: Forecast impact experiment with GPS radio occultation measurements. Geophys. Res. Lett., 32, 1–4, doi:10.1029/2004GL020806.
Ho, S., M. Goldberg, Y. Kuo, C. Zou, and W. Schreiner, 2009: Calibration of Temperature in the Lower Strato sphere from Micro wave Measurements Using COSMIC Radio Occultation Data : Preliminary Results. 20, 87–100, doi:10.3319/TAO.2007.12.06.01(F3C)1.
Ho, S., Y.H. Kuo, W. Schreiner, and X. Zhou, 2010: Using SI‐traceable Global Positioning System radio occultation measurements for climate monitoring. Bull. Am. Meteorol. Soc., 91, S36–S37, doi:10.1175/BAMS-91-7-StateoftheClimate.
Hoffman, R. N., and R. Atlas, 2016: Future Observing System Simulation Experiments. Bull. Am. Meteorol. Soc., 97, 1601–1616, doi:10.1175/BAMS-D-15-00200.1. http://journals.ametsoc.org/doi/10.1175/BAMS-D-15-00200.1.
Honda, T., and Coauthors, 2018: Assimilating all-sky Himawari-8 satellite infrared radiances: A case of Typhoon Soudelor (2015). Mon.Wea. Rev., 146, 213–229, doi:10.1175/MWR-D-16-0357.1.
Houtekamer, P. L., and H. L. Mitchell, 1998: Data Assimilation Using an Ensemble Kalman Filter Technique. Mon.Wea. Rev., 126, 796–811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.
——, and F. Zhang, 2016: Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation. Mon.Wea. Rev., 144, 4489–4532, doi:10.1175/MWR-D-15-0440.1.
——, X. Deng, H. L. Mitchell, S. J. Baek, and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter. Mon.Wea. Rev., 142, 1143–1162, doi:10.1175/MWR-D-13-00138.1.
Houtekamer, P. L., M.Buehner, andM.DeLa Chevrotière, 2019: Using the hybrid gain algorithm to sample data assimilation uncertainty. Q. J. R. Meteorol. Soc., 145, 35–56, doi:10.1002/qj.3426.
Huang, C.-Y., Y.-H. Kuo, S.-H. Chen, and F. Vandenberghe, 2005: Improvements in Typhoon Forecasts with Assimilated GPS Occultation Refractivity. Weather Forecast., 20, 931–953, doi:10.1175/WAF874.1.
——, and Coauthors, 2010: Impact of GPS radio occultation data assimilation on regional weather predictions. GPS Solut., 14, 35–49, doi:10.1007/s10291-009-0144-1.
——, S.-Y. Chen, S. K. A. V. P. Rao Anisetty, S.-C. Yang, and L.-F. Hsiao, 2016: An Impact Study of GPS Radio Occultation Observations on Frontal Rainfall Prediction with a Local Bending Angle Operator. Weather Forecast., 31, 129–150, doi:10.1175/WAF-D-15-0085.1.
Kalnay, E., and S. C.Yang, 2010: Accelerating the spin-up of Ensemble Kalman filtering. Q. J. R. Meteorol. Soc., 136, 1644–1651, doi:10.1002/qj.652.
——, H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007: 4-D-Var or ensemble Kalman filter? Tellus A Dyn. Meteorol. Oceanogr., 59, 758–773, doi:10.1111/j.1600-0870.2007.00261.x.
Kleist, D. T., 2012: An evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. University of Maryland, College Park, 149pp.
Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results. Mon.Wea. Rev., 143, 433–451, doi:10.1175/MWR-D-13-00351.1.
Kondo, K., and T. Miyoshi, 2016: Impact of removing covariance localization in an ensemble Kalman Filter: Experiments with 10 240 members using an intermediate AGCM. Mon.Wea. Rev., 144, 4849–4865, doi:10.1175/MWR-D-15-0388.1.
Kong, R., M. Xue, and C. Liu, 2018: Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments. Mon.Wea. Rev., 146, 175–198, doi:10.1175/MWR-D-17-0164.1.
Kueh, M.-T., C.-Y. Huang, S.-Y. Chen, S.-H. Chen, and C.-J. Wang, 2009: Impact of GPS Radio Occultation Refractivity Soundings on a Simulation of Typhoon Bilis (2006) upon Landfall. Terr. Atmos. Ocean. Sci., 20, 115, doi:10.3319/TAO.2008.01.21.03(F3C).
Kuo, Y.-H., T.-K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. .Anthes, 2004: Inversion and Error Estimation of GPS Radio Occultation Data. J. Meteorol. Soc. Japan, 82, 507–531, doi:10.2151/jmsj.2004.507.
Kuo, Y. H., W. S. Schreiner, J. Wang, D. L. Rossiter, and Y. Zhang, 2005: Comparison of GPS radio occultation soundings with radiosondes. Geophys. Res. Lett., 32, 1–4, doi:10.1029/2004GL021443.
Kursinski, E. R., G. A. Hajj, J. T. Schofield, R. P. Linfield, and K. R. Hardy, 1997: Observing Earth’s atmosphere with radio occultation measurements using the Global Positioning System. J. Geophys. Res. Atmos., 102, 23429–23465, doi:10.1029/97JD01569.
Lange, H., and G. C.Craig, 2014: The impact of data assimilation length scales on analysis and prediction of convective storms. Mon.Wea. Rev., 142, 3781–3808, doi:10.1175/MWR-D-13-00304.1.
VanLeeuwen, P. J., 2009: Particle filtering in geophysical systems. Mon.Wea. Rev., 137, 4089–4114, doi:10.1175/2009MWR2835.1.
Leon, S. J., Å. Björck, and W. Gander, 2013a: Gram-Schmidt orthogonalization: 100 years and more. Numer. Linear Algebr. with Appl., 20, 492–532, doi:10.1002/nla.1839. http://doi.wiley.com/10.1002/nla.1839.
——, ——, and——, 2013b: Gram-Schmidt orthogonalization: 100 years and more. Numer. Linear Algebr. with Appl., 20, 492–532, doi:10.1002/nla.1839.
Lin, K.-J., S.-C. Yang, and S. S.Chen, 2018: Reducing TC position uncertainty in ensemble data assimilation and prediction system: A Case Study of Typhoon Fanapi (2010). Weather Forecast., 33, 561–582, doi:10.1175/waf-d-17-0152.1.
Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP - A comparison with 4D-Var. Q. J. R. Meteorol. Soc., 129, 3183–3203, doi:10.1256/qj.02.132.
——, 2013: Recommended Nomenclature for EnVar Data Assimilation Methods. WGNE Blue B. Res. Act. Atmos. Ocean. Model.,.
Ménétrier, B., and T. Auligné, 2015: Optimized Localization and Hybridization to Filter Ensemble-Based Covariances. Mon.Wea. Rev., 143, 3931–3947, doi:10.1175/MWR-D-15-0057.1.
Millan, R. M., and Coauthors, 2019: Small satellites for space science: A COSPAR scientific roadmap. Adv. Sp. Res., 64, 1466–1517, doi:10.1016/j.asr.2019.07.035.
Minamide, M., and F. Zhang, 2017: Adaptive observation error inflation for assimilating all-Sky satellite radiance. Mon.Wea. Rev., 145, 1063–1081, doi:10.1175/MWR-D-16-0257.1.
Mitchell, H. L., and P. L. Houtekamer, 2000: An Adaptive Ensemble Kalman Filter. Mon.Wea. Rev., 128, 416, doi:10.1175/1520-0493(2000)128<0416:AAEKF>2.0.CO;2.
Miyoshi, T., and K. Kondo, 2013: A multi-scale localization approach to an ensemble Kalman filter. Sci. Online Lett. Atmos., 9, 170–173, doi:10.2151/sola.2013-038.
——, ——, and T. Imamura, 2014: The 10,240-member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett., 41, 5264–5271, doi:10.1002/2014GL060863.
Morss, R. E., 1999: Adaptive observations: Idealized sampling strategies for improving numerical weather prediction. Ph.D. thesis, Massachusetts Institute of Technology, 225pp.
Parrish, D. F., and J. C.Derber, 1992: The National Meteorological Center’s spectral statistical- interpolation analysis system. Mon.Wea. Rev., 120, 1747–1763, doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.
Penny, S. G., 2014: The hybrid local ensemble transform Kalman filter. Mon.Wea. Rev., 142, 2139–2149, doi:10.1175/MWR-D-13-00131.1.
——, 2017: Mathematical foundations of hybrid data assimilation from a synchronization perspective. Chaos, 27, doi:10.1063/1.5001819.
——, and T. Miyoshi, 2016: A local particle filter for high-dimensional geophysical systems. Nonlinear Process. Geophys., 23, 391–405, doi:10.5194/npg-23-391-2016.
——, D. W. Behringer, J. A. Carton, and E. Kalnay, 2015: A Hybrid Global Ocean Data Assimilation System at NCEP. Mon.Wea. Rev., 143, 4660–4677, doi:10.1175/MWR-D-14-00376.1.
Pires, C., R. Vautard, and O. Talagrand, 1996: On extending the limits of variational assimilation in nonlinear chaotic systems. Tellus A Dyn. Meteorol. Oceanogr., 48, 96–121, doi:10.3402/tellusa.v48i1.11634.
Poli, P., P. Moll, D. Puech, F. Rabier, and S. B. Healy, 2009: Quality Control , Error Analysis , and Impact Assessment of FORMOSAT-3/COSMIC in Numerical Weather Prediction. Terr. Atmos. Ocean. Sci., 20, 101–113, doi:10.3319/TAO.2008.01.21.02(F3C)1.
Poli, P., S. B. Healy, and D. P. Dee, 2010: Assimilation of Global Positioning System radio occultation data in the ECMWF ERA-Interim reanalysis. Q. J. R. Meteorol. Soc., 136, 1972–1990, doi:10.1002/qj.722.
Satterfield, E. A., D. Hodyss, D. D. Kuhl, and C. H. Bishop, 2018: Observation-Informed Generalized Hybrid Error Covariance Models. Mon.Wea. Rev., 146, 3605–3622, doi:10.1175/MWR-D-18-0016.1.
Schreiner, W. S., J. P. Weiss, R. A. Anthes, J. Braun, V. Chu, J. Fong, D. Hunt, Y.‐H. Kuo, T. Meehan, W. Serafino, J. Sjoberg, S. Sokolovskiy, E. Talaat, T.K. Wee, Z. Zeng, 2020: COSMIC‐2 radio occultation constellation: First results. Geophys. Res. Lett., 47, e2019GL086841. https://doi.org/10.1029/2019GL086841.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.‐Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3, NCAR Tech. Note NCAR/TN–475+STR ,USA, 113 pp. doi:10.5065/D68S4MVH.
Snyder, C., T. M. Hamill, and S. B. Trier, 2003: Linear evolution of error covariances in a quasigeostrophic model. Mon.Wea. Rev., 131, 189–205, doi:10.1175/1520-0493(2003)131<0189:LEOECI>2.0.CO;2.
Storto, A., P. Oddo, A. Cipollone, I. Mirouze, and B. Lemieux-Dudon, 2018: Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation. Ocean Model., 128, 67–86, doi:10.1016/j.ocemod.2018.06.005.
Tavolato, C., and L.Isaksen, 2015: On the use of a Huber norm for observation quality control in the ECMWF 4D-Var. Q. J. R. Meteorol. Soc., 141, 1514–1527, doi:10.1002/qj.2440.
Toth, Z., and E. Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Am. Meteorol. Soc., 74, 2317–2330, doi:10.1175/1520-0477(1993)074<2317:efantg>2.0.co;2.
Waller, J. A., D. Simonin, S. L. Dance, N. K. Nichols, and S. P. Ballard, 2016: Diagnosing observation error correlations for doppler radar radial winds in the met office UKV model using observation-minus-background and observation-minus-analysis statistics. Mon.Wea. Rev., 144, 3533–3551, doi:10.1175/MWR-D-15-0340.1.
Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble - 3DVAR hybrid analysis schemes. Mon.Wea. Rev., 135, 222–227, doi:10.1175/MWR3282.1.
——, D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-Based Ensemble–Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments. Mon.Wea. Rev., 141, 4098–4117, doi:10.1175/MWR-D-12-00141.1.
Ware, R., and Coauthors, 1996: GPS sounding of the atmosphere from low earth orbit: Preliminary results. Bull. Am. Meteorol. Soc., 77, 19–40, doi:10.1175/1520-0477(1996)077<0019:GSOTAF>2.0.CO;2.
Weston, P. P., W. Bell, and J. R. Eyre, 2014: Accounting for correlated error in the assimilation of high-resolution sounder data. Q. J. R. Meteorol. Soc., 140, 2420–2429, doi:10.1002/qj.2306.
Wu, P., S. Yang, C. Tsai, and H. Cheng, 2020: Convective-scale sampling error and its impact on the ensemble radar data assimilation system: A case study of heavy rainfall event on 16th June 2008 in Taiwan. Mon. Wea. Rev., in press, doi:10.1175/MWR-D-19-0319.1.
Yang, S.-C., E. Kalnay, B. Hunt, and N.E. Bowler, 2009a: Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter. Q. J. R. Meteorol. Soc., 135, 251–262, doi:10.1002/qj.353.
——, S.-H. Chen, S.-Y. Chen, C.-Y. Huang, and C.-S. Chen, 2014: Evaluating the Impact of the COSMIC RO Bending Angle Data on Predicting the Heavy Precipitation Episode on 16 June 2008 during SoWMEX-IOP8. Mon.Wea. Rev., 142, 4139–4163, doi:10.1175/MWR-D-13-00275.1.
——, ——, K. Kondo, T. Miyoshi, Y.-C. Liou, Y.-L. Teng, and H.-L. Chang, 2017: Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system. J. Adv. Model. Earth Syst., 9, 1684–1702, doi:10.1002/2017MS001009.
——, M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2009b: Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model. Mon.Wea. Rev., 137, 693–709, doi:10.1175/2008MWR2396.1.
——, E. Kalnay, and B. Hunt, 2012a: Handling nonlinearity in an ensemble Kalman filter: Experiments with the three-variable lorenz model. Mon.Wea. Rev., 140, 2628–2646, doi:10.1175/MWR-D-11-00313.1.
——, ——, and T. Miyoshi, 2012b: Accelerating the EnKF spinup for typhoon assimilation and prediction. Weather Forecast., 27, 878–897, doi:10.1175/WAF-D-11-00153.1.
——, ——, and T. Enomoto, 2015: Ensemble singular vectors and their use as additive inflation in EnKF. Tellus, Ser. A Dyn. Meteorol. Oceanogr., 67, 1–20, doi:10.3402/tellusa.v67.26536.
Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009a: Cloud-resolving hurricane initialization and prediction through assimilation of doppler radar observations with an ensemble Kalman filter. Mon.Wea. Rev., 137, 2105–2125, doi:10.1175/2009MWR2645.1.
——, M. Zhang, and J. A.Hansen, 2009b: Coupling ensemble Kalman filter with four-dimensional variational data assimilation. Adv. Atmos. Sci., 26, 1–8, doi:10.1007/s00376-009-0001-8.
——, ——, and J. Poterjoy, 2013: E3DVar: Coupling an ensemble kalman filter with three-dimensional variational data assimilation in a limited-area weather prediction model and comparison to E4DVar. Mon.Wea. Rev., 141, 900–917, doi:10.1175/MWR-D-12-00075.1.
Zou, X., H. Liu, R. A. Anthes, H. Shao, J. C. Chang, and Y. J. Zhu, 2004: Impact of CHAMP radio occultation observations on global analysis and forecasts in the absence of AMSU radiance data. J. Meteorol. Soc. Japan, 82, 533–549, doi:10.2151/jmsj.2004.533.
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